按数据实际贡献定价，让高质量数据值更多钱。
Utility-Aware Data Pricing: Token-Level Quality and Empirical Training Gain for LLMs
- 用熵和质量分评估每个词元的有用性
- 实测训练增益显示新方法排名准确率超基线
- 支持可验证、防篡改的数据交易记录
基于“行数×质量系数”的传统数据估值方法无法捕捉数据对大语言模型能力的非线性贡献。本文提出一种动态数据估值框架，从静态计数转向基于效用的定价。方法包含三层：(1) 使用香农熵和数据质量分计算词元级信息密度；(2) 通过影响函数、代理模型和数据沙普利值实测训练增益；(3) 利用哈希承诺、默克尔树和防篡改训练账本实现密码学可验证性。在指令遵循、数学推理和代码摘要三个真实场景中进行充分实验，结果显示基于代理模型的实测增益与实际效用排名高度一致，显著优于行数和词元数基线。该框架支持公平的数据即服务经济，使高推理价值数据按其真实贡献定价，并保障数据市场的透明与可审计。
原文摘要 · Abstract (English)
Traditional data valuation methods based on ``row-count $\times$ quality coefficient'' paradigms fail to capture the nuanced, nonlinear contributions that data makes to Large Language Model (LLM) capabilities. This paper presents a dynamic data valuation framework that transitions from static accounting to utility-based pricing. Our approach operates on three layers: (1) token-level information density metrics using Shannon entropy and Data Quality Scores; (2) empirical training gain measurement through influence functions, proxy model strategies, and Data Shapley values; and (3) cryptographic verifiability through hash-based commitments, Merkle trees, and a tamper-evident training ledger. We provide comprehensive experimental validation on three real domains (instruction following, mathematical reasoning, and code summarization), demonstrating that proxy-based empirical gain achieves near-perfect ranking alignment with realized utility, substantially outperforming row-count and token-count baselines. This framework enables a fair Data-as-a-Service economy where high-reasoning data is priced according to its actual contribution to model intelligence, while providing the transparency and auditability necessary for trustworthy data markets.
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